I’ve observed data and analytics often times get a reputation for being extremely complicated. Individuals who have made careers in the data and analytics space are often referred to as some name associated with magic: wizards, magicians, or miracle workers. While data and analytics can be VERY complicated (Tesla’s software for autonomous driving being an example), there are also many ways to leverage data and analytics in your business that are incredibly simple.
If you’re a business owner who hasn’t started leveraging analytics and are a little intimidated by the complexity of analytics, my hope is to help you feel more comfortable with the idea of starting simple. There is still value to be gotten out of analytics with these simple approaches. Once you’ve gotten the simple approach down, then you can build upon that to gain more and more value from analytics.
Analytics Maturity Model
A commonly used framework to explain analytics maturity is the analytics maturity model. There are a number of variations of the below graph, but I prefer this one from Gartner as its simple and includes the questions trying to be answered at each maturity stage.
To illustrate what this maturity model looks like in the real world, I’ll use this from the perspective of a local restaurant.
Stage 1: Descriptive Analytics
Stages 2, 3, and 4 are all based on how well you can do stage 1. You need to be able to know what has happened in the past to understand why it happened, and then predict what will happen. This makes descriptive analytics very important, as it sets the foundation for what we usually view as the more complicated (and valuable) analytics.
Descriptive analytics is often referred to as reporting. It tells you what your sales and costs were over the last 12 months, how many customers you had, and what your website traffic was. Understanding the past puts you in a better position to advance down the analytics maturity path, both from a knowledge and understanding perspective, but also from a data perspective. The same data you use for descriptive analytics is used for the more advanced analytics stages.
For our restaurant example, this includes labor costs, inventory, rent, food cost and sales, etc. This data is then put together to show the restaurants profits. By putting it into the below graph, we can see most months they’re making a profit.
This is great information, but what can you do with it? By simply showing this type of data without any context provides essentially no value outside of:
- In March the restaurant lost money
- In July and September the restaurant had a minimal profit
- In January, May, October, November, and December the restaurant made a large profit
If you stop with descriptive analytics, the next questions are likely to be “why should I care?” or “so what?”.
Stage 2: Diagnostic Analytics
Some business owners may view analytics as a replacement for the intimate knowledge they have of the inner workings of their business. This couldn’t be further from the truth. The diagnostic analytics phase is a great of example of how powerful bringing analytics and contextual knowledge together is.
The diagnostic analytics phase is answering the question “why did this happen?”, which is often the step in the analytics journey you start seeing real value and return on your investment. If you’re collecting a lot of data in your business, you can look further into the data to start trying to find the why. However, this type of data collection isn’t always done, so applying your contextual knowledge can be a great (and sometimes better) substitute.
The owner of the restaurant suspects the presence of college students in town may be a significant reason for some of the changes in profit. The owner gets some estimates from others in the community for how many college students are in town for that month. When you put that profit and % of college students in town together on the same graph, you can see the profits and college students being in town generally follow each other from month to month. Its safe to conclude the changes in college students coming and going throughout the year plays a significant role in the profits for the restaurant.
With this insight on the relationship between profit and college students being in town, now we have something that is actionable and can be used to improve profits! Can we change our advertising focus when college students leave? Should we reduce our costs during these slower months? Should we run specials targeted at the non-student population? We’re able to get to this insight without any complicated statistics, models, or autonomous driving AI.
Stages 3 and 4: Predictive and Prescriptive Analytics
Since the goal of this post is to focus on how analytics can be simple, I’ll be brief about predictive and prescriptive analytics. These by nature are more complex, however if you’ve set the appropriate foundation with descriptive and diagnostic analytics, you’ll find these to be less complicated to understand.
The goal of predictive analytics is straightforward, being able to predict the future with some degree of certainty. Prescriptive analytics however can be a bit harder to understand. Think of prescriptive analytics as what diagnostic analytics is to descriptive analytics: moving from “cool information, but what do I do” to “here is why and what we can do to change it”. Predictive analytics will tell you what to expect in the future, while prescriptive analytics will tell you what is driving that prediction and then how to influence it.
Using the restaurant example, you can predict sales for the next year and get a number. What happens if you don’t like that number and want to know how to increase sales? Prescriptive analytics uses the predictive model and tells you what is driving that the predictions. This should sound similar to what diagnostic analytics is (because it is). The key difference is diagnostic analytics is a lot simpler and faster to get started than prescriptive analytics.
In short, analytics can be relatively easy and fast to start getting value out of it. There is a general model you can reference for analytics maturity to understand the difference levels of analytics. While the model isn’t perfect, as each business varies from the next, it provides a general guideline for analytics. If you’re pursuing the analytics journey, expect the initial stages to feel less and less complicated as you continue to use them. This then opens the door for more advanced analytics (that hopefully don’t feel as advanced as they do today)!
Interested in pursuing analytics in your business? Fill out our contact form and we’ll set up some time to discuss how Simplified Analytics may be able to help!